Due to the high resolution and sensitivity, the emission tomographic scanner is becoming the instrument of choice in human functional study. The strength of the tomographic scanner is its ability to qualify the activity distribution of radionuclides in the human body. The accuracy of the quantification depends on an adequate correction of the raw data. In the following, a PET system is used as an example for illustration purpose.
Attenuation and scatter corrections in PET are considered by the nuclear medicine community as a vital component in the production of accurate quantitative data. The attenuation problem can be corrected exactly in PET and limited mainly by the statistics of the acquired data. The correction of the scattered radiation in PET is one of the most challenging tasks.
The PET system utilizes the coincidence detection of a pair of gamma rays generated by the annihilation interaction of a positron and an electron. To increase the sensitivity, the septa device (used to collimate against scatter radiation) is always removed for a 3D PET scan. The scatter events are increased significantly, it is estimated that scatter fraction increases from 14% to 36% when the septa was removed. Scattered events cause the misjudgment of source location and downgrade system's spatial resolution. They add a background to the true distribution and result in reducing the contrast and quantitatively overestimating the actual activity in the reconstructed radioactivity concentrations. Rejection of scattered photons on the basis of energy discrimination has limited performance because the use of relatively wide energy window to maintain accurate counting statistics.
Correction for scatter remains essential not only for quantification but also for lesion detection and image segmentation. The impact of scatter on image generally depends on the energy resolution of the detectors and energy window settings, the object size, shape and chemical composition, and the source distribution. Many of these parameters are non-stationary, which implies a potential difficulty when developing proper scatter correction techniques.
Accurate scatter correction is one of the major problems facing quantitative 3D PET and still is an open question. Much research and development has been concentrated on the scatter compensation required for quantitative 3D PET. The difference among the correction methods is the way in which the scatter component is estimated. They mainly fall into four broad categories: energy window based approaches, curve fitting based approaches, convolution based approaches, and reconstruction based scatter compensation approaches.
Multiple energy window methods have been in use for many years for SPECT. Recent advances in PET acquisition mode and improvement in the detector energy resolution enable the implementation of scatter correction based on energy spectra. This method assumes the high energy (photopeak) window contains both primary and scatter event while the low energy window contains majority of scattered events. There exist a fixed scatter ratio between the total scattered events and low energy window events. From a linear combination of the two data sets, the scattered events conceal. The use of a constant scatter ratio for larger objects might not be adequate. The ratio also varies with the attenuation coefficient of the material. As a result, this method will not adequately handle non-uniform objects. Another disadvantage is that some commercial systems do not allow acquisition of coincidence events in separate windows.
The curve fitting technique is based on the assumption that the scatter spatial distribution can be described by a Gaussian function or second-order polynomials. The scatter at the center is interpolated from the region outside the source object. The assumption could be problematic for scans of large, inhomogeneous regions of the body. Based on similar scheme, this technique compares further the difference between a 3D (high scatter) and 2D (low scatter) scan to estimate the scatter contamination. This method requires an additional 2D scan.
The convolution approaches estimate the distribution of scatter from the standard photopeak data. It uses constant scatter kernels which are parametrized by mono-exponential or Gaussian functions. The scatter distribution is estimated by iteratively convolving the photopeak projections with the kernel. The disadvantages are that it does not consider scatter originating from outside the field of view (FOV) and that the kernels are measured with phantoms that may not adequately represent human anatomy.
The reconstruction based approaches, estimate the scatter component using a rigorous Monte Carlo simulation. The scatter component can be estimated directly from emission and transmission data using Monte Carlo simulation. Ollinger calculates the single-scatter distribution directly using the Klien-Nishina formula and convolutes this scatter distribution to estimate the multiple-scatter. These approaches require large amounts of computational power and processing time due to the repetitive looping over many parameters.
In the disclosure of U.S. Pat. No. 6,590,213, Scott D. Wollenweber improved the speed of execution of model-based scatter algorithms by combining axial data within certain ranges into composite transaxial planes or “super-slices” and thus effectively collapsing data along the axial direction. By so combining the axial data, one can perform the model-based scatter algorithms by looping over the in-plane parameters x and y within each super-slice, instead of looping over both the in-plane parameters and the azimuthal angle dimension. By eliminating the calculations associated with looping over the azimuthal angle dimension, the computation time required for performing the method-based scatter algorithms is reduced.
In the disclosure of U.S. Pat. No. 5,903,008, Jianying Li invented an emission tomographic system for imaging an object of interest. The system, in one form, includes a gantry and a patient table. A detector including a collimator is secured to the gantry, and a computer is coupled to the gantry and to the detector to detect and control the position of the detector relative to the table. The system is configured to determine a transmission measurement and generate a scatter fraction utilizing the transmission measurement. A dual energy window data acquisition algorithm then determines non-scatter photons in a primary energy window utilizing the scatter fraction.
Most scatter correction methods above perform Monte Carlo simulations to calculate the transmission of scattered radiation indirectly. And there are some information, such as body structures and distribution of source activity, that should be known. However, it's impossible to obtain the precise information that the results are not really exactly.